Non-expert scientists are frequently involved in research requiring data acquisition over large geographic areas. Despite mutual benefits for such “citizen science”, barriers also exist, including 1) difficulty maintaining user engagement with timely feedback, and 2) the challenge of providing non-experts with the means to generate reliable data. We have developed a system that addresses these barriers. Our technologies, KnoWare and InSpector, allow users to: collect reliable scientific measurements, map geo-tagged data, and intuitively visualize the results in real-time. KnoWare comprises a web portal and an iOS app with two core functions. First, users can generate scientific ‘queries’ that entail a call for information posed to a crowd with customized options for participant responses and viewing data. Second, users can respond to queries with their GPS-enabled mobile device, which results in their geo- and time-stamped responses populating a web-accessible map in real time. KnoWare can also interface with additional applications to diversify the types of data that can be reported. We demonstrate this capability with a second iOS app called InSpector that performs quantitative water quality measurements. When used in combina-tion, these technologies create a workflow to facilitate the collection, sharing and interpretation of scientific data by non-expert scientists.

The main aim of this paper is to provide a theoretically and empirically grounded discussion on IT-based organizational creativity support. This study attempts to answer the following questions: (1) what is the issue of organizational creativity and its IT-based support, (2) what is the demand for IT –based organizational creativity support; (3) what are the main determinants and barriers to IT-based organizational creativity support; and (4) what success factors are crucial for IT-based organizational creativity support. This paper presents the analysis results of a survey conducted in 25 selected organizations. The paper provides valuable information on the possibilities of IT applications in organizational creativity support as well as the associated success factors. It makes useful contribution to our better understanding of IT-based organizational creativity support issues.

Aim/Purpose: To develop and introduce a questionnaire that investigates the informing needs, information-seeking behavior, and supplier selection of procurement officers in Israel. The questionnaire’s internal consistency reliability is given. Additionally, we describe the demographic description of the procurement officers in Israel. Background: Procurement science is an important field that affects firms’ profits in the private sector and is significant to growth, innovation, sustainability, and welfare in the public sector. There is little research about the informing needs of procurement officers in general and particularly in Israel. Methodology: A quantitative questionnaire that is sent to all the procurement officers in Israel’s procuring association. Contribution: The questionnaire that is developed in this paper may be used by other researchers and practitioners to evaluate the information needs of procurement officers. Findings: The typical procurement officer is male, with a bachelor degree and is digitally proficient. Recommendations for Practitioners: The procuring side can use the questionnaire to develop better tools for obtaining information efficiently. The supplying side can use this knowledge to improve its exposure to potential customers and address its customer’s needs better. Recommendation for Researchers: The questionnaire can address theoretical questions such as how digital literacy affects the procuring process and provide empirical findings about active research areas such as supplier selection and information-seeking behavior. Future Research: Future research will examine the relationship between the various variables and demographic features to understand why specific information needs and information-seeking behaviors arise.

Aim/Purpose : An important objective of any firm is escalation of its performance and the achievement of competitive advantages. Supply chain agility plays a prominent role to enhance the level of firm’s performance. Moreover, information technology (IT) plays a foundational role in supply chain management practices. Hence, this study proposes the relationship between IT integration as the competency of IT and firm’s market performance both directly and through mediating role of supply chain agility. Background: Many studies have been done to date on the impact of supply chain agility on overall firm’s performance. However, the effect of an agile supply chain on firm’s market performance per se needs to be studied. Furthermore, there is a gap in the literature about the effect of IT competency such as IT integration on firm’s market performance both directly and through mediating role of supply chain agility. Recommendation for Researchers: The first direction this study gives to researchers is to consider the different factors which have significant effect on the agility of supply chain, particularly the IT related ones. The second direction is about the study on the effect of IT competencies and supply chain agility on each category of firm’s performance separately instead of considering it as a one construct. Impact on Society : Although this is a conceptual study, it can highlight the importance of IT competency not only in our daily life, but also in our businesses and industries. Future Research: This study only proposes some relationships based on theory and literature. Future researchers can test these proposed relationships in different contexts and compare the results. Furthermore, this study proposes the relationships for large manufacturing sector in developing countries. The model could be tested for SMEs as well. In addition, the proposed theoretical model in this study might be tested in both developing as well as developed countries to compare the results which will be contributed to the body of knowledge.

Aim/Purpose: Personal Knowledge Management (PKM) has been envisaged as a crucial tool for the growing creative class of knowledge workers, but adequate technological solutions have not been forthcoming. Background: Based on former affordance-related publications (primarily concerned with communication, community-building, collaboration, and social knowledge sharing), the common and differing narratives in relation to PKM are investigated in order to suggest further PKM capabilities and affordances in need to be conferred. Methodology: The paper follows up on a series of the author’s PKM-related publications, firmly rooted in design science research (DSR) methods and aimed at creating an innovative PKM concept and prototype system. Contribution: The affordances presented offer PKM system users the means to retain and build upon knowledge acquired in order to sustain personal growth and facilitate productive collaborations between fellow learners and/or professional acquaintances. Findings: The results call for an extension of Nonaka’s SECI model and ‘ba’ concept and provide arguments for and evidence supporting the claims that the PKM concept and system is able to facilitate better knowledge traceability and KM practices. Recommendations and Impact on Society: Together with the prior publications, the paper points to current KM shortcomings and presents a novel trans-disciplinary approach offering appealing opportunities for stakeholders engaged in the context of curation, education, research, development, business, and entrepreneurship. Its potential to tackle opportunity divides has been addressed via a PKM for Development (PKM4D) Framework. Future DSR Activities: After completing the test phase of the prototype, its transformation into a viable PKM system and cloud-based server based on a rapid development platform and a noSQL-database is estimated to take 12 months.

Aim/Purpose: With information almost effortlessly created and spontaneously available, current progress in Information and Communication Technology (ICT) has led to the complication that information must be scrutinized for trustworthiness and provenance. Information systems must become provenance-aware to be satisfactory in accountability, reproducibility, and trustworthiness of data. Background: Multiple models for abstract representation of provenance have been proposed to describe entities, people, and activities involved in producing a piece of data, including the Open Provenance Model (OPM) and the World Wide Web Consortium. These models lack certain concepts necessary for specifying workflows and encoding the provenance of data products used and generated. Methodology: Without loss of generality, the focus of this paper is on OPM depiction of provenance in terms of a directed graph. We have redrawn several case studies in the framework of our proposed model in order to compare and evaluate it against OPM for representing these cases. Contribution: This paper offers an alternative flow-based diagrammatic language that can form a foundation for modeling of provenance. The model described here provides an (abstract) machine-like representation of provenance. Findings: The results suggest a viable alternative in the area of diagrammatic representation for provenance applications. Future Research: Future work will seek to achieve more accurate comparisons with current models in the field.

Aim/Purpose: The aim of this paper is to identify the possibilities for reducing the second and third levels of the digital divide (or inequality) through conscious application of digital technologies, especially through the promotion of digital means for information, enlightenment, and entertainment. Background: This article reviews studies carried out between 2000 and 2017, which investigate the social benefits of digital technology use for disadvantaged user groups and, especially, of their outcomes in terms of increasing digital skills and motivation to use information and communication technologies. Methodology: The literature review of the selected texts was carried out using thematic content analysis. The coding scheme was open but based on the theory of three levels of digital divide by van Dijk. Contribution: The results of the analysis show the difficulties related to the attempts of reducing the digital divide on the second and third level using only digital interventions, but also reveal the potential of these interventions. Findings: The literature review confirms the connection of different levels of digital divide with other relational and structural inequalities. It provides insights into the strengths and weaknesses of digital interventions aimed at the reduction of digital inequalities. Their success depends on the consideration of the context and participants needs as well as on carefully planned strategies. The paper summarizes and demonstrates the shortcomings and limitations of poorly designed interventions in reducing the digital divide but emphasizes the possibilities of raising the motivation and benefits for the participants of strategically planned and implemented projects. Recommendations for Practitioners: While planning a digital intervention with the aim of reducing digital inequalities, it is necessary to assess carefully the context and the needs of participants. Educational interventions should be based on suitable didactic and learning strategies. Recommendation for Researchers: More research is needed into the factors that increase the effectiveness of digital interventions aimed at reducing the digital divide. Future Research: We will apply the findings of this literature review in an intervention in the context of Lithuanian towns of different sizes.

Aim/Purpose: This paper describes the regulatory affairs discipline as a useful case in the study of both inter- and transdisciplinary science and dynamics related to communication across multiple boundaries. We will 1) outline the process that led to the development of transnational competencies for regulatory affairs graduate education, 2) discuss how the process highlights the transdisciplinary character of regulatory affairs, 3) provide implications for how to communicate the influence of this characterization to future healthcare professionals, and 4) draw conclusions regarding how our lessons-learned might inform other programs of study. Background: In the past few decades, the regulatory affairs profession has become more internationalized. This prompted the need for new competencies grounded in the transnational and cross-disciplinary contexts in which these professionals are required to operate. Methodology: A convenience sample of experienced regulatory affairs professionals from multiple disciplines contributed to the development of transnational competencies for a master’s program in regulatory affairs using a transdisciplinary framework. Contribution: An applied exemplar in which to understand how transdisciplinary characteristics can be communicated and applied in higher education. Recommendations for Practitioners: This paper recommends how competencies developed from a regulatory affairs program can serve as exemplars for other applied transdisciplinary higher education programs. Impact on Society: This framework provides a seldom-used reflective approach to regulatory affairs education that utilizes cross-disciplinary theory to inform competence-based formation of professionals.

Aim/Purpose: This paper explores the implications of machine-mediated communication on human interaction in cross-disciplinary teams. The authors explore the relationships between Open Science Theory, its contributions to team science, and the opportunities and challenges associated with adopting open science principles. Background: Open Science Theory impacts many aspects of human interaction throughout the scholarly life cycle and can be seen in action through various technologies, which each typically touch only one such aspect. By serving multiple aspects of Open Science Theory at once, the Open Science Framework (OSF) serves as an exemplar technology. As such it illustrates how Open Science Theory can inform and expand cognitive and behavioral dynamics in teams at multiple levels in a single tool. Methodology: This concept paper provides a theoretical rationale for recommendations for exploring the connections between an open science paradigm and the dynamics of team communication. As such theory and evidence have been culled to initiate a synthesis of the nascent literature, current practice and theory. Contribution: This paper aims to illuminate the shared goals between open science and the study of teams by focusing on science team activities (data management, methods, algorithms, and outputs) as focal objects for further combined study. Findings: Team dynamics and characteristics that will affect successful human/machine assisted interactions through mediators of workflow culture, attitudes about ownership of knowledge, readiness to share openly, shifts from group-driven to user-driven functionality, group-organizing to self-organizing structures, and the development of trust as teams regulate between traditional and open science dissemination. Recommendations for Practitioners: Participation in open science practices through machine-assisted technologies in team projects/scholarship should be encouraged. Recommendation for Researchers: The information provided highlights areas in need of further study in team science as well as new primary sources of material in the study of teams utilizing machine-assisted methods in their work. Impact on Society: As researchers take on more complex social problems, new technology and open science practices can complement the work of diverse stakeholders while also providing opportunities to broaden impact and intensify scholarly contributions. Future Research: Future investigation into the cognitive and behavioral research conducted with teams that employ machine-assisted technologies in their workflows would offer researchers the opportunity to understand better the relationships between intelligent machines and science teams’ impacts on their communities as well as the necessary paradigmatic shifts inherent when utilizing these technologies.

Aim/Purpose: Transdisciplinarity is considered as a framework for understanding knowledge producing teams (KPTs). Features of transdisciplinary knowledge producing teams (TDKPTs) are provided using a complex adaptive systems (CAS) lens. TDKPT features are defined and linked to complexity theory to show how team participants might develop skills that more truly express complex adaptive conditions. Background: TDKPTs are groups of stakeholder participants tasked with producing knowledge across disciplinary, sectoral, and ecological boundaries. TDKPTs reflect components of complex adaptive systems (CAS) and exemplify how CAS behave and function. Methodology: The paper accesses literature from the Science-of-Team-Science (SciTS), complexity theory, and systems theory to construct a typology of the features of TDKPTs. Contribution: This paper provides a list of features developed from a diverse body of literature useful for considering complexity within TDKPTs. Findings: The paper proposes a series of features of transdisciplinary knowledge producing teams. In addition, the authors identify important skill building aspects needed for TDKPTs to be successful. Recommendations for Practitioners: The paper provides a framework by which team functioning can be considered and enhanced within TDKPTs. Recommendation for Researchers: The paper suggests categorical features of transdisciplinary teams for research on the collaborative processes and outcomes of TD teams. Future Research: Knowledge producing team members need to engage in theoretical, episte-mological, and methodological reflections to elucidate the dynamic nature of TD knowledge producing teams. Understanding how conflict, dissonance, and reciprocal interdependencies contribute to knowledge generation are key areas of future research and inquiry.

Aim/Purpose: Provides a theoretical model as to where we should source our information as the environment becomes more complex. Background: Develops a theoretical model built on extrinsic complexity and offers a conceptual scheme relating to the relative value of different sources. Methodology: The paper is purely conceptual in nature. Contribution: Develops a model that could be tested relating to where clients should search for information. Findings: Arguments can be made that different environments warrant different priorities for informing sources. Recommendations for Practitioners: Assess how your sources of information match your perceived environment. Recommendation for Researchers: Consider developing research designs to test the proposed model. Impact on Society: Offers a new way of thinking about informing sources. Future Research: Develop propositions from the model that could be empirically tested in future research.

Aim/Purpose: The year 2020 marks the 50th anniversary of the first official definition of the term “transdisciplinarity.” This paper focuses on a critical analysis of the development of modern transdisciplinarity since its inception. Background: The article presents two main directions for the development of transdisciplinarity. It also shows its identification features, strengths, and weaknesses, as well as the significant role transdisciplinarity plays in science and education. Methodology: The methodology employed in this article is a content analysis of resolutions of international forums as well as articles on transdisciplinarity published from 1970 to 2019. Contribution: For one reason or the other, several of these authors did not quote the opinions of the original authors of transdisciplinarity. The subsequent use of those articles by other authors thus posed some ambiguities about the place and role of transdisciplinarity in science and education. The advent of e-databases has made it possible to access the original forum articles. This further made it possible to refine the original content of the term “transdisciplinarity” and to trace its development without mixing it with vague opinions. Based on these findings, the perception of transdisciplinarity as a marginal trend in science and education could be eliminated. Findings: This paper shows how modern transdisciplinarity is developing into two main directions: transdisciplinarity in science as well as transdisciplinarity in education. These orientations have individual goals and objectives. The transdisciplinarity of scientific research helps to complete the transformation of the potential for interdisciplinary interaction and the integration of disciplines. Whereas, in education, transdisciplinarity (meta-discipline) is about developing an international standard for transdisciplinary education and also describing the content of transdisciplinary competence for students of diverse disciplines at all levels of higher education (bachelor’s, master’s and postgraduate studies). Recommendation for Researchers: Transdisciplinary research involves the interaction of people with disciplinary knowledge plus a degree of scientific outlook. Since disciplinary knowledge domains remain in their disciplinary boxes, it is, therefore, advisable to generalize disciplinary knowledge rather than force them to interact. This is the basis for proposing the systems transdisciplinary approach—which provides a methodology for unifying and generalizing disciplinary knowledge. Future Research: As the research shows, the organizers of modern international forums do not take into account the division of transdisciplinarity development trends. To increase the effectiveness and significance of such forums, it is necessary to return to the practice of organizing special international forums on the transdisciplinarity of science and that of education.

Aim/Purpose: Absence of new scientific approaches and specialists (generalists), who professionally obtain such approaches, is one of the main reasons for an ineffective solution of complex multifactor problems of the modern society. Background: The article briefly describes the concept of systems transdisciplinary integration of knowledge of different scientific disciplines. Also, it shows an opportunity to use this concept education of generalists in higher education. Methodology: The article highlights the idea of gestalt of knowledge, which is based on systems transdisciplinary model of spatial unit of order. It describes the basis of gestalt-of-the-one and gestalt-of-the-whole. Also, it explains the differences and practical capabilities of holist generalists and unicentrist generalists. Contribution: Loss of identificational attributes can take place during the process of integration of knowledge of different scientific disciplines. The article shows how to avoid this complication within a systems transdisciplinary approach. Findings: Each type of fundamental knowledge has its own carriers, such as scientists and specialists. Therefore, direct interaction of people-carriers of fundamental knowledge has limited potential. Presently, a more practical importance is the interaction between scientists and specialists within the zones of hybridization of fundamental knowledge. Hybridization is the process of systematization of knowledge within specialized systems transdisciplinary models of unit of order. A specialist generalist’s professional work is to organize scientific research, systemise knowledge of different scientific disciplines, make necessary conclusions, and suggest optimal solution for complex multifactor problems. Therefore, generalists should be considered as an important move towards the solution of complex multifactor problems of modern society. Recommendation for Researchers: A new scientific approach is a way of widening scientific worldview. A new approach in inorganic chemistry made it possible to create the Mendeleev periodic table of elements. Owing to this table, researchers were able to learn the characteristics and attributes of chemical elements, which can be found in nature. Also, models of systems transdisciplinary approach allow the discovery of new elements and relations of complex multifactor problems. Its absence would, however, hinder the research and the problem description. Future Research: The article justifies that preparation of generalists in higher education is one of the main peculiarities of universities of the third generation. Therefore, it might be desirable for organizers of higher education and university leaders to begin speculations regarding this quest, develop educational programs for generalists, and search for optimal forms and methods of solution.

Aim/Purpose: The growing complexity of the business environment and business processes as well as the Big Data phenomenon has an impact on every area of human activity nowadays. This new reality challenges the effectiveness of traditional narrowly oriented professional education. New areas of competences emerged as a synergy of multiple knowledge areas – transdisciplines. Informing Science and Data Science are just the first two such new areas we may identify as transdisciplines. Universities are facing the challenge to educate students for those new realities. Background: The purpose of the paper is to share the authors’ experience in designing curriculum for training bachelor students in Informing Science as a concentration within an Information Brokerage major, and a master program on Data Science. Methodology: Designing curriculum for transdisciplines requires diverse expertise obtained by both academia and industries and passed through several stages - identifying objectives, conceptualizing curriculum models, identifying content, and development pedagogical priorities. Contribution: Sharing our experience acquired in designing transdiscipline programs will contribute to a transition from a narrow professional education towards addressing 21st-century challenges. Findings: Analytical skills, combined with training in all categories of so-called “soft skills”, are essential in preparing students for a successful career in a transdiciplinary area of activities. Recommendations for Practitioners: Establishing a working environment encouraging not only sharing but close cooperation is essential nowadays. Recommendations for Researchers: There are two aspects of training professionals capable of succeeding in a transdisciplinary environment: encouraging mutual respect and developing out-of-box thinking. Impact on Society: The transition of higher education in a way to meet current challenges. Future Research The next steps in this research are to collect feedback regarding the professional careers of students graduating in these two programs and to adjust the curriculum accordingly.

Aim/Purpose: In a world of rapidly expanding complexity and exponentially increasing data availability, IT-based knowledge management tools will be needed to manage and curate available information. This paper looks at a particular tool architecture that has been previously proposed: The Personal Knowledge Management System (PKMS). The specific focus is on how the proposed architecture conforms to design science principles that relate to how it is likely to evolve. Background: We first introduce some recent informing science and design science research frameworks, then examine how the PKMS architecture would conform to these. Methodology: The approach taken is conceptual analysis. Contribution: The analysis provides a clearer understanding of how the proposed PKMS would serve the diverse-client ambiguous-target (DCAT) informing scenario and how it could be expected to evolve. Findings: We demonstrate how the PKMS informing architecture can be characterized as a “social machine” that appears to conform to a number of principles that would facilitate its long-term evolution. Future Research: The example provided by the paper could serve as a model future research seeking to integrate design science and informing science in the study of IT artefacts.

Aim/Purpose: The aim is to explore the informing flows framework as interactions within a PhD education practicing a work-integrated learning approach in order to reveal both the perspectives of industrial PhD students and of industry. Background: An under-researched field of university-industry collaboration is explored revealing both the perspectives of industrial PhD students and of industry. Methodology: Qualitative methods were applied including interviews and document studies. In total ten semi-structured interviews in two steps were conducted. The empirical context is a Swedish PhD program in informatics with a specialization in work-integrated learning. Contribution: By broadening the concept of work-integrated learning, this paper contributes empirical results on benefits and challenges in university-industry collaboration focusing on industrial PhD students and industry by applying the informing flows framework. Findings: Findings expose novel insights for industry as well as academia. The industrial PhD students are key stakeholders and embody the informing flows between practice and university and between practice and research. They are spanning boundaries between university and industry generating continuous opportunities for validation and testing of empirical results and models in industry. This may enable increased research quality and short-lag dissemination of research results as well as strengthened organizational legitimacy. Recommendation for Researchers: Academia is recommended to recognize the value of the industrial PhD students’ pre-understanding of the industry context in the spirit of work-integrated learning approach. The conditions for informing flows between research and practice need to continuously be maintained to enable short-term societal impact of research for both academia and industry. For practitioners: This explorative study show that it is vital for practice to recognize that challenges do exist and need to be considered to strengthen industrial PhD pro-grams as well as university-industry collaborations. Additionally, it is of importance to formalize a continuously dissemination of research in the industries. Future Research: Future international and/or transdisciplinary research within this field is encouraged to include larger samples covering other universities and a mix of industrial contexts or comparing industrial PhD students in different phases of their PhD education.

Aim/Purpose: As traditional Knowledge Management (KM) struggles to support the personal needs of knowledge workers in a new era of accelerating information abundance, we examine the shortcomings and put forward alternative scenarios and architectures for developing a novel Personal KM System (PKMS). Background: While prior publications focused on the complementing features compared to conventional dynamic KM models, our emphasis shifts to instantiating a flourishing PKMS community supported by a Digital Platform Ecosystem. Methodology: Design science research focusing on conceptual analysis and prototyping. Contribution: The PKMS concept advances the understanding of how digital platform communities may serve members with highly diverse skills and ambitions better to gainfully utilize the platform’s resources and generative potential in their personal and local settings. Findings: We demonstrate how the needs to tackle attention-consuming rising entropy and to benefit from generative innovation potentials can be addressed. Future Research: As this article has iteratively co-evolved with the preparing of a PKMS implementation, business, and roll-out plan, the prototype’s testing, completion, and subsequent migration to a viable system is of primary concern.

Aim/Purpose: There is a need to find a way to utilize narrative storytelling in film to make students more aware of the impacts of global problems and how they are perceived. Background: Two films from the year 2015 from two very different places in the world explore the encroachment and secondary effects of urban civilization upon indigenous cultures. Methodology: An interpretive, qualitative, methodology was used in addressing and discussing the use of these two films as a persuasive communication teaching aid. Contribution: This paper offers an approach to using narratives of films on indigenous issues in education to inform students about real-world issues and the wide impacts of those on various cultures and populations. Findings: Through the discussion of the two films, we suggest that using films with indigenous themes is beneficial to a course curriculum in a variety of subjects from communication to history and politics, to help students visualize the problems at hand. Anecdotally, the authors note that students are more engaged and willing to discuss topics if they have watched films or clips that deal with those topics than if they have simply read about them. Recommendation for Researchers: Technology and use of visuals are used as teaching tools in a variety of fields. Film narratives can be used as a teaching tool in multiple fields and provide insight about a variety of ideas. Identifying films such as those with indigenous themes provides an example of how one film can bring up multiple, real-world, topics and through led discussion student reflection can potentially lead to self-insights and have lasting impacts. Future Research: Additional research and assessment can be done on the impact of teaching with films and their compelling story telling of issues, and what types of questions should be asked to maximize learning and the impact of film narratives.

Aim/Purpose: The research problem of this study refers to the manner in which old and new mass media represented the significant social development surrounding two crashes of the Boeing 737 MAX airplane. Methodology: The study follows a qualitative case study methodology based on a sample of newspaper articles, TV programming, specialized technical publications, Twitter posts, and Facebook content. Contribution: The study contributes to understanding specifics and differences in representing extraordinary socio-economic events by different types of media. Findings: Key findings are that these media have constructed different realities surrounding the tragic events and exhibited informing distortions to different degrees. Recommendations for Practitioners: Practical implications of this study are relevant for the institutional and individual clients of informing with regard to selecting appropriate media for use. There are also implications for informers with regard to reducing distortions in informing. Recommendation for Researchers: Social media could be a channel for alternative learning rather than manipulation. Mainstream media were confirmed to be a loudspeaker for authorities as postulated in critical media research, and analytical media provided influential, deeper technical analysis. Future Research: As the Boeing case unfolds, it would be interesting to investigate any evolution in mediated realities.

Aim/Purpose: To discuss the Design Science Research approach by comparing some of its canons with observed practices in projects in which it is applied, in order to understand and structure it better. Background: Recent criticisms of the application of the Design Science Research (DSR) approach have pointed out the need to make it more approachable and less confusing to overcome deficiencies such as the unrealistic evaluation. Methodology: We identified and analyzed 92 articles that presented artifacts developed from DSR projects and another 60 articles with preceding or subsequent actions associated with these 92 projects. We applied the content analysis technique to these 152 articles, enabling the preparation of network diagrams and an analysis of the longitudinal evolution of these projects in terms of activities performed and the types of artifacts involved. Contribution: The content analysis of these 152 articles enabled the preparation of network diagrams and an analysis of the longitudinal evolution of these projects in terms of the activities and types of artifacts involved. Evidence was found of a precedence hierarchy among different types of artifacts, as well as nine new opportunities for entry points for the continuity of DSR studies. Only 14% of the DSR artifacts underwent an evaluation by typical end users, characterizing a tenth type of entry point. Regarding the evaluation process, four aspects were identified, which demonstrated that 86% of DSR artifact evaluations are unrealistic. Findings: We identified and defined a set of attributes that allows a better characterization and structuring of the artifact evaluation process. Analyzing the field data, we inferred a precedence hierarchy for different artifacts types, as well as nine new opportunities for entry points for the continuity of DSR studies. Recommendation for Researchers: The four attributes identified for analyzing evaluation processes serve as guidelines for practitioners and researchers to achieve a realistic evaluation of artifacts. Future Research: The nine new entry points identified serve as an inspiration for researchers to give continuity to DSR projects.

Aim/Purpose: This study seeks to uncover the perceived quality factors of computer-mediated communication in business planning in which communication among teammates is crucial for collaboration. Background: Computer-mediated communication has made communicating with teammates easier and more affordable than ever. What motivates people to use a particular CMC technology during business planning is a major concern in this research. Methodology: This study seeks to address the issues by applying the concept of Information Product Quality (IPQ). Based on 21 factors derived from an extensive literature review on Information Product Quality (IPQ), an experimental study was conducted to identify the factors that are perceived as most relevant. Contribution: The findings in this study will help developers find a more customer-oriented approach to developing CMC technology design, specifically useful in collaborative work, such as business planning. Findings: This study extracted the three specific quality factors to use CMC technology in business planning: informational, physical, and service. Future Research: Future research will shed more light on the generality of these findings. Future studies should be extended to other population and contextual situations in the use of CMC.

Aim/Purpose: Labeling a journal as “predatory” can do great damage to the journal and the individuals that have contributed to it. This paper considers whether the predatory classification has outlived its usefulness and what might replace it. Background: With the advent of open access publishing, the term “predatory” has increasingly been used to identify academic journals, conferences, and publishers whose practices are driven by profit or self-interest rather than the advancement of science. Absent clear standards for determining what is predatory and what is not, concerns have been raised about the misuse of the label. Methodology: Mixed methods: A brief review of the literature, some illustrative case studies, and conceptual analysis. Contribution: The paper provides recommendations for reducing the impact of illegitimate journals. Findings: Current predatory classifications are being assigned with little or no systematic research and virtually no accountability. The predatory/not predatory distinction does not accommodate alternative journal missions. Recommendations for Researchers: The distinction between legitimate and illegitimate journals requires consideration of each journal’s mission. To serve as a useful guide, a process akin to that used for accrediting institutions needs to be put in place. Impact on Society: Avoiding unnecessary damage to the careers of researchers starting out. Future Research: Refining the initial classification scheme proposed in the paper.

The purpose of this paper is to provide a model to help explain why ideas about reality differ. Misinformation is an important topic that in the past several years has gained prominence. The author developed a model of informing.

Aim/Purpose This paper aims to illustrate, through an exploratory ideographic case study, how a Complex Thinking framework can inform the design of scientific events and the facilitation of scientific Inter and Transdisciplinary groups towards positive emergent outcomes, both at the level of the functioning of the group and the collective complexity of their thinking. Moreover, it aims to show how the choice of facilitation strategies can contribute to positive emergent outcomes in the context of a fully online event, with its inherent constraints. Finally, this study aims to conduct an exploratory qualitative evaluation of the participants’ experiences during School, with a focus on the processes and how they relate to the aims of the School and the goals of the facilitation. Background Science needs to embrace modes of knowing capable of generating more complex (differentiated, integrated, recursively organized, emergent), ecologically fit, and creative responses, to meet the complexity of the world’s challenges. New formats and strategies are required that attend to the facilitation of Inter and Transdisciplinary scientific events and meetings, towards creative and complex outcomes. A Complex Thinking framework provides suggestions for the facilitation of Inter and Transdisciplinary meetings and events through targeting key properties which may lead to the emergence of complex and creative outcomes. Methodology We adopt an ideographic case study approach to illustrate how a complex systems approach, in particular a Complex Thinking framework, grounded in an enactive view of cognition, guided the design choices and the facilitation strategies of an online Inter and Transdisciplinary Advanced Training School (Winter School). We aim to illustrate how the facilitation strategies were selected and used to promote deep and creative interactions within the constraints of an online environment. We adopt an exploratory qualitative approach to investigate the participants’ reports of their experiences of the School, in light of the principles and goals that guided its design and facilitation. Contribution This paper opens a new area of theoretical and applied research, under the scope of a Complex Thinking framework, focused on the facilitation of Inter and Transdisciplinarity at scientific events, meetings, and discussions towards complex and creative outcomes. Findings The results of the exploratory qualitative analysis of the participants’ experiences regarding the event suggest a critical role of its methodology in fostering rich, deep, and constructive interactions, in leading to the emergence of a collective group experience, to the integration of ideas, and in facilitating transformative personal experiences, under the effects of the emergent group processes. It suggests that the strategies employed were successful, anticipating and overcoming the particular constraints of an online event. Recommendations for Practitioners This case study suggests that a Complex Thinking framework can fruitfully guide the design of facilitation strategies and activities for scientific events and meetings, activating a number of key relational processes that contribute to or boost the emergence of positive group experiences and the production and integration of novel ideas. Recommendations for Researchers This study calls for action-oriented and applied research focused on the developmental evaluation of innovations, regarding the facilitation of scientific creativity and integration, within the scope of a Complex Thinking approach. Impact on Society This paper calls for new modes of organization and formats of scientific activities, suggesting that Inter and Transdisciplinary events and meetings may benefit from intentional management and facilitation of interactions between participants to produce transformative impacts. It demonstrates the importance of the organizational principles used to plan and run events that engage multiple and various societal agents, from academics to practitioners and social activists, towards enhancing their richness and relevance to complex real-world challenges. Future Research This study highlights the need for process-focused systematic case study research using complex systems-informed designs to explore how and which facilitation strategies may promote which (interaction of) properties of Complex Thinking and associated processes and how, and under which conditions, these lead to more complex and creative outcomes.

Aim/Purpose: A literature review is often criticized for the absence of coherent construction, synthesis of topics, and well-reasoned analysis. A framework is needed for novice researchers to organize and present information obtained from the literature review. Background: Information and communication technologies advancement have yielded overwhelming information. The massive availability of information poses several challenges, including storage, processing, meaningful organization, and presentation for future consumption. Information System Researchers have developed frameworks, guidelines, and tools for gathering, filtering, processing, storing, and organizing information. Interestingly, information system researchers have vast information that needs meaningful organization and presentation to the research fraternity while conducting a literature review on a research topic. Methodology: This paper describes a framework called LACTiC (Location, Author, Continuum, Time, and Category) that we adapted from another framework called LATCH (Location, Alphabetical, Time, Category, and Hierarchy). LATCH was used to organize and present information on e-commerce websites for seamless navigation. We evaluated the LACTiC framework. Contribution: Information System Researchers can use the LACTiC framework to organize information obtained from literature review. Findings: The evaluation reveals that most researchers from information systems organize information obtained from the literature review category-wise, followed by continuum, author, time, and location. Recommendation for Researchers: Overall, the framework works well and can be helpful for researchers for an initial idea for organizing information obtained from the literature review. Future Research: To conceptualize the framework, the study was carried out using Information Systems related literature. To generalize the proposed framework, we may suggest that the study can be extended to other areas of business management, such as marketing, finance, operation, decision sciences, accounting, and economics.

Aim/Purpose: The present study aims to frame the relationship between job and personal resources (namely, organizational identification and emotional intelligence), burnout, and musculoskeletal disorders (i.e., back pain, upper limb pain, lower limb discomfort), into the theoretical framework provided by the JD-R health model. Background: Empirical research indicates a connection between burnout and the onset of musculoskeletal problems, one of the most important occupational health issues affecting all jobs and organizations. In light of the JD-R health model, we investigated the association between personal and job resources with burnout and musculoskeletal disorders. Methodology: An anonymous online questionnaire was answered by 320 workers (82.4% female, Mage = 42.18; SDage = 12.24) investigating their perceived level of burnout, the presence of musculoskeletal pain (back, neck, and shoulder), and their level of organizational identification and emotional intelligence. Descriptive analysis, correlation, and moderated mediation model were performed using SPSS. Contribution: We confirmed the role of personal and organizational resources in the salutogenic process considered by the JD-R health model. Emotional intelligence, decreasing the perceived level of burnout, limited the development of musculoskeletal disorders. Moreover, when organizational identification presented low and medium levels, the association between emotional intelligence and burnout strengthened. Findings: Our results showed a negative, indirect effect of emotional intelligence on musculoskeletal disorders via burnout. Moreover, we found a moderation of organizational organization, indicating that at low and medium levels of identification, the association between emotional intelligence and burnout is stronger. Recommendation for Researchers: In addition to work factors involved in the link between burnout and musculoskeletal disorders, it is also important to consider personal and emotional factors, which can decrease the occurrence of adverse consequences. Future Research: Future research developments could contribute to a deeper understanding of the mechanisms linking emotional intelligence, burnout, and musculoskeletal problems, as well as consider objective indicators of burnout levels or consider using ecological data collection methodologies (e.g., ecological momentary assessment), to identify patterns and associations between burnout and musculoskeletal disorders.

Aim/Purpose: To explore the incidence of addiction potential within the Iranian public working population, describing how many Iranian public employees fall within the diagnostic categories of low, moderate, and high addiction potential. Also, to investigate the predicting role of occupational variables such as perceived stress, job security, and job satisfaction on addiction potential and belonging to low, moderate, and high addiction potential diagnostic categories. Background: Substance addiction among employees can lead to several negative consequences at the individual and organizational levels. Also, it is the fourth cause of death in Iran. However, few studies have been conducted on the topic among employees, and non among Iranian employees. Methodology: The study participants were 430 employees working in governmental offices of the North Khorasan province, Iran. Descriptive statistical analysis and multiple linear regression analysis were conducted to explore the incidence of addiction potential within the analyzed population and to investigate whether occupational variables such as perceived stress, job security, and job satisfaction predicted low, moderate, or high addiction potential. Contribution: This paper suggests that perceived stress might act as a risk factor for developing addiction, whereas job security and job satisfaction might be protective factors against the likelihood of addiction development. Findings: More than half of the sample showed moderate to high addiction potential. Perceived stress was positively related to addiction potential. Job security and job satisfaction were negatively related to addiction potential. Recommendation for Researchers: When addressing the topic of substance addiction, researchers should focus on the preventative side of investigating it; that is, addiction risk rather than already unfolded addiction. Also, researchers should be mindful of the cultural context in which studies are conducted. Future Research: Future research might investigate other relevant occupational predictors in relation to employee addiction potential, such as leadership style, work-life balance, and worktime schedule, or expand on the relevant causal chain by including personality traits such as neuroticism.

Aim/Purpose: The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background: The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology: In our work, we analyze various machine learning algorithms and implement K-NN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution: To the researchers’ best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings: An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendation for Researchers: With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Future Research: Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages.

Aim/Purpose: The study aims to examine the mediating role of job motivation and affective and normative commitment on the relationship between perceived organizational support (POS) and job turnover intention. Background: POS refers to employees’ beliefs and perceptions concerning the extent to which the organization values their contributions, cares about their well-being, and fulfils their socio-emotional needs. To date, research has shown that employee turnover is a complex construct resulting from the interplay of both individual and organizational variables, such as motivation and climate. Methodology: Cross-sectional data were collected from 143 employees of an Italian industrial company. Paper-and-pencil questionnaires were used to assess respondents’ POS, job motivation, affective and normative organizational commitment, and turnover intentions. Contribution: Specifically, in this research, we aim at examining (i) the indirect effect of POS on turnover intention via (ii) job motivation and (iii) normative and affective commitment. Findings: Results show that high POS is associated with high levels of job motivation and affective and normative commitment, which in turn are negatively linked to turnover intentions. Recommendation for Researchers: Researchers should not lose sight of the importance of studying and delving into the concept of turnover intention given that, from an organizational point of view, losing personnel means losing competencies, which need to be replaced through assessment, selection, training, and development, processes that are often challenging and expensive. Future Research: Future research should further investigate the role of motivation and commitment, other than additional variables, for POS and turnover intention. Longitudinal studies and further testing are required to verify the causal processes stemming from our model. Future research could consider linking employees’ self-reported measures with objective data concerning turnover rates.

Aim/Purpose: This is an introductory paper for the developing special series on applied psychology and informing science. It takes into account the spirit of informing science to launch the first of three articles in the series on applied psychology. The paper concludes by raising questions for future investigations.

Aim/Purpose: This paper applies informing science principles to analyze the evolution of United States (U.S.) healthcare delivery, exploring how policy shifts, technological advancements, and changing practices have transformed informing processes within this complex system. By examining healthcare delivery through a transdisciplinary lens, we aim to enhance the understanding of intricate informing environments and their dynamics. Background: The U.S. healthcare system epitomizes a complex, evolving transdisciplinary domain intersecting information systems, policy, economics, and public health. Recent transformations in stakeholder information flow necessitate an informing science perspective to comprehend these changes fully. Methodology: We synthesize literature on U.S. healthcare delivery changes, employing informing science frameworks such as Cohen’s “informing environment” concept to analyze the evolution of healthcare informing processes. Contribution: This study expands informing science theory by examining how changes in a complex transdisciplinary system impact information flow, decision-making, and stakeholder interactions. The results provide insights into challenges and opportunities within evolving informing environments. Findings: Our analysis reveals significant alterations in the U.S. healthcare informing landscape due to policy, regulatory, and technological changes. We identify key transformations in client-sender-delivery system relationships, shifts in information asymmetry, and the emergence of novel informing channels and barriers. Recommendation for Researchers: Future studies should develop informing science models capable of capturing the complexity and dynamism of healthcare delivery systems, particularly amidst rapid technological and policy changes. Future Research: Further investigation is needed into how emerging technologies reshape healthcare informing processes and their impact on care quality, accessibility, and cost-effectiveness.

Aim/Purpose: This study aims to explore students’ digital entrepreneurial intention (DEI) in Kuwait. Specifically, the aim is twofold: (i) to identify and examine the factors influencing and predicting students’ DEI, and (ii) to validate a model of DEI. Background: The advent of modern digital technologies has provided entrepreneurs with many opportunities to establish and expand their firms through online platforms. Although the existing literature on DEI has explored various factors, certain factors that could be linked to DEI have been neglected, and others have not been given sufficient attention. Nonetheless, there has been little research on students’ DEI, particularly in Kuwait. Methodology: To fulfill the research’s aims, a study was conducted using a quantitative method (a survey of 305 students at a non-profit university in Kuwait). Contribution: This study aimed to fill the research gap on the limited DEI research among Kuwait’s students. Several recommendations were suggested to improve the DEI among students in Kuwait. Findings: The study identified five factors that could influence an individual’s intention to engage in digital entrepreneurship. These factors include self-perceived creativity, social media use, risk-taking and opportunity recognition, digital entrepreneurship knowledge, and entrepreneurial self-perceived confidence. Significant solid correlations were between all five identified factors and DEI. However, only self-perceived creativity and entrepreneurial self-perceived confidence were identified as significant positive predictors of DEI among undergraduates in Kuwait. Nevertheless, the main contributor to this intention was the students’ self-perceived confidence as entrepreneurs. Recommendation for Researchers: Researchers should conduct further longitudinal studies to understand better the dynamic nature of DEI and execution. Future Research: Additional research is required to utilize probability sampling approaches and increase the sample size for more generalizable findings.

Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

Aim/Purpose: The truism is that leadership fosters or restricts innovation behaviours in organisations, but the extent to which it does depends on the leadership style in practice. This study focuses on one of the contemporary leadership styles, knowledge-oriented leadership [KOL], which has received scant attention in research. In doing so, the contextual factors of psychological safety [PS] and employee voice [EV] were applied to determine how KOL influences are channeled to innovation at the individual level. Methodology: Data were collected from 347 academic staff in public universities in Southern Nigeria and subjected to a partial least square [PLS] analytical procedure for data treatment and hypotheses testing using the SmartPLS 3 software for variance-based structural equation modelling. Contribution: The study formed an integrated research framework that links knowledge-oriented leadership and innovation by accounting for the contextual mechanisms of psychological safety and employee voice. Findings: The PLS results demonstrated that the knowledge-oriented leadership and innovation relationship was positive and significant, and this relationship was partially mediated by two variables, namely, PS and EV. Furthermore, the two mediating variables channeled KOL’s influence on innovation in a sequence. Recommendation for Researchers: Organisations need to consider the practical application of KOL to improve innovation outcomes considerably. By this, leadership training programs should include modules, courses, or topics on KOL to engender the formation of requisite managerial skills. More so, they should consider the criterion of demonstrable KOL abilities for leadership selection and recruitment. As a personal development initiative, managers can attend leadership development programmes as well as obtain certification in knowledge management to improve their KOL abilities. This initiative should be encouraged and supported by organisations. In all, the human resource management framework should be responsive to the dynamics of the knowledge economy regarding leadership. Given that PS and EV function as mediators, organisations should actively cultivate an environment enabling interpersonal risky behaviours founded on trust, respect, and cooperation and encourage/support employees who demonstrate such behaviour accordingly. In this line, they should create and sustain a supportive environment that positively reinforces voice decisions and behaviours. Future Research: The study only determined the links between KOL, PS, EV, and innovation in public universities in Southern Nigeria. Other studies may examine the linkages in other knowledge-intensive organisations as well as expand the geographic scope to make for better generality of findings. Future studies should look at other underlying mechanisms that can affect the KOL-innovation relationship, such as psychological capital, work engagement, work commitment, etc. The role of moderators can be identified and introduced to this integrative framework to demonstrate the conditions affecting the linkages.

Labor pain and its relieving methods is one of the anxieties of mothers having a great impact on the quality of care during delivery as well as the patients' satisfaction. The propensity of using non-medicinal pain relief methods is increasing. The present study aimed to compare the effect of Acupressure at two GB-21 and SP06 points on the severity of labor pain. In this quasi-experimental single blind study started on December 2010 and ended on June 2011 in which 150 primiparous women were divided into three groups of Acupressure at GB-21 point, Acupressure at SP-6 point and control group. The intervention was carried out for 20 min at 3-4 and 20 min at 7-8 cm dilatation of Cervix. The pain severity was measured by Visual Analog Scale before and immediately, 30 and 60 min after the intervention. Then, the data were statistically analyzed. No significant difference was found among the 3 groups regarding the pain severity before the intervention. However, the pain severity it was reduced at 3-4 and 7-8 cm dilatation immediately, 30 and 60 min after the intervention in the two intervention groups compared to the control group (p<0.001). Nonetheless, no statistically significant difference was observed between the two intervention groups (p = 0.93). The results of the study showed that application of Acupressure at two GB-21 and SP-6 points was effective in the reduction of the severity of labor pain. Therefore, further studies are recommended to be performed on the application of Acupressure together with non-medicinal methods.

This paper proposes an efficient solution for personalised web directories recommendation using fast FCM+DQN. At first, web directory usage file obtained from given dataset is fed into the accretion matrix computation module, where visitor chain matrix, visitor chain binary matrix, directory chain matrix and directory chain binary matrix are formulated. In this, directory grouping is accomplished based on fast FCM and matching among query and group is conducted based on Kumar Hassebrook and Kulczynski similarity. The user preferred directory is restored at this stage and at last, personalised web directories are recommended to the visitors by means of DQN. The proposed approach has received superior results with respect to maximum accuracy of 0.910, minimum mean squared error (MSE) of 0.0206 and root mean squared error (RMSE) of 0.144. Although the system offered magnificent outcomes, it failed to order web directories in the form of highly, medium and low interested directories.

Mental illnesses are common among college students as well as their non-student peers, and the number and severity of these problems are increasing. It can be difficult to identify people suffering from mental illness and get the help they need early. So in this paper, the SqueezeR_MobileNet method is proposed. It performs feature fusion and early mental health prediction. Initially, outliers in the input data are detected and removed. After that, using missing data imputation and Z-score normalisation the pre-processing phase is executed. Next to this, for feature fusion, a combination of the Soergel metric and deep Kronecker network (DKN) is used. By utilising bootstrapping data augmentation is performed. Finally, early mental health prediction is done using SqueezeR_MobileNet, which is the incorporation of residual SqueezeNet and MobileNet. The devised approach has reached the highest specificity of 0.937, accuracy of 0.911 and sensitivity of 0.907.

This work demonstrates a hybrid deep learning (DL) model for lung cancer (LC) detection using CT images. Firstly, the input image is passed to the pre-processing stage, where the input image is filtered using a BF and the obtained filtered image is subjected to lung lobe segmentation, where segmentation is done using squeeze U-SegNet. Feature extraction is performed, where features including entropy with fuzzy local binary patterns (EFLBP), local optimal oriented pattern (LOOP), and grey level co-occurrence matrix (GLCM) features are mined. After completing the extracting of features, LC is detected utilising the hybrid efficient-ShuffleNet (HES-Net) method, wherein the HES-Net is established by the incorporation of EfficientNet and ShuffleNet. The presented HES-Net for LC detection is investigated for its performance concerning TNR, and TPR, and accuracy is established to have acquired values of 92.1%, 93.1%, and 91.3%.

This paper presents a novel deep learning technique called quantum dilated convolutional neural network-DenseNet (Q-DenseNet) for prediction of heart disease in spark framework. At first, the input data taken from the database is allowed for data partitioning using fast fuzzy C-means clustering (FFCM). The partitioned data is fed into spark framework, where pre-processed by missing data imputation and quantile normalisation. The pre-processed data is further allowed for selection of suitable features. Then, the selected features from the slave nodes are merged and fed into master node. The Q-DenseNet is used in master node for the prediction of heart disease. The performance improvement of the designed Q-DenseNet model is validated by comparing with traditional prediction models. Here, the Q-DenseNet method achieved superior performance with maximum of 92.65% specificity, 91.74% sensitivity, and 90.15% accuracy.

An optimisation-based decision-making support is proposed in this study in the form of fuzzy-probabilistic programming, which can be used to solve integrated order allocation, production planning, and inventory management problems in fuzzy and probabilistic uncertain environments. The problem was modelled in an uncertain mathematical optimisation model with two objectives: maximising the expectation of production volume and minimising the expectation of total operational cost subject to demand and other constraints. The model belongs to fuzzy-probabilistic bi-objective integer linear programming, and the generalised reduced gradient method combined with the branch-and-bound algorithm was utilised to solve the derived model. Numerical simulations were performed to illustrate how the optimal decision was formulated. The results showed that the proposed decision-making support was successful in providing the optimal decision with the maximum expectation of the production volume and minimum expectation of the total operational cost. Therefore, the approach can be implemented by decision-makers in manufacturing companies.

Data envelopment analysis (DEA) is widely used in various fields and for various models. Inverted data envelopment analysis (inverted DEA) is an extended model of DEA. Regression analysis (RA) is a statistical process for estimating the relationships among variables based on the model of averaged image. There are no essential relations among DEA and RA and inverted DEA. We creatively combine DEA, RA and inverted DEA to propose a new model: DEA-RA-Inverted DEA model. The model realises the efficiency estimation and evaluation through a discussion of the residual variables and the residual ratio coefficients. In addition, we will demonstrate the effectiveness of the model by applying it to efficiency estimation and evaluation of 16 Chinese logistics enterprises.

This study addresses a critical chain project scheduling (CCPS) problem regarding the feeding buffer. The main contribution of this study lies in determining the critical chain when the feeding buffer is considered along with the project buffer, a less addressed issue in the critical chain literature. Using a mixed-integer nonlinear programming (MINLP) model, the critical chain of a project with no break-down and no overflow is found. Moreover, the impact of the feeding buffer on the criticality of activities is discussed. The problem is solved using the Lingo software package for validation in small-sized instances. Since the CCPS is known as an NP-hard problem, a genetic algorithm (GA) is also designed to solve large-scale instances. The algorithm's performance is confirmed using various project scheduling library test problems. Sensitivity analysis is implemented based on some crucial parameters, and the critical chain is analysed after conducting several experiments. It is shown how considering the feeding buffer makes different critical chains and how shortlisting activities and resources are optimally managed.

Congcong FANG,Yun JIN,Guanlin CHEN,Yunfan ZHANG,Shidang LI,Yong MA,Yue XIE, Vol.E107-D, No.11, pp.1463-1467
Currently, an increasing number of tasks in speech emotion recognition rely on the analysis of both speech and text features. However, there remains a paucity of research exploring the potential of leveraging large language models like GPT-3 to enhance emotion recognition. In this investigation, we harness the power of the GPT-3 model to extract semantic information from transcribed texts, generating text modal features with a dimensionality of 1536. Subsequently, we perform feature fusion, combining the 1536-dimensional text features with 1188-dimensional acoustic features to yield comprehensive multi-modal recognition outcomes. Our findings reveal that the proposed method achieves a weighted accuracy of 79.62% across the four emotion categories in IEMOCAP, underscoring the considerable enhancement in emotion recognition accuracy facilitated by integrating large language models.
Publication Date: 2024/11/01

Nat PAVASANT,Takashi MORITA,Masayuki NUMAO,Ken-ichi FUKUI, Vol.E107-D, No.11, pp.1453-1457
We proposed a procedure to pre-process data used in a vector autoregressive (VAR) modeling of a temporal point process by using kernel density estimation. Vector autoregressive modeling of point-process data, for example, is being used for causality inference. The VAR model discretizes the timeline into small windows, and creates a time series by the presence of events in each window, and then models the presence of an event at the next time step by its history. The problem is that to get a longer history with high temporal resolution required a large number of windows, and thus, model parameters. We proposed the local density estimation procedure, which, instead of using the binary presence as the input to the model, performed kernel density estimation of the event history, and discretized the estimation to be used as the input. This allowed us to reduce the number of model parameters, especially in sparse data. Our experiment on a sparse Poisson process showed that this procedure vastly increases model prediction performance.
Publication Date: 2024/11/01

Keitaro NAKASAI,Shin KOMEDA,Masateru TSUNODA,Masayuki KASHIMA, Vol.E107-D, No.11, pp.1444-1448
To automatically measure the mental workload of developers, existing studies have used biometric measures such as brain waves and the heart rate. However, developers are often required to equip certain devices when measuring them, and can therefore be physically burdened. In this study, we evaluated the feasibility of non-contact biometric measures based on the nasal skin temperature (NST). In the experiment, the proposed biometric measures were more accurate than non-biometric measures.
Publication Date: 2024/11/01

Nan WU,Xiaocong LAI,Mei CHEN,Ying PAN, Vol.E107-D, No.11, pp.1433-1443
With the development of the Semantic Web, an increasing number of researchers are utilizing ontology technology to construct domain ontology. Since there is no unified construction standard, ontology heterogeneity occurs. The ontology matching method can fuse heterogeneous ontologies, which realizes the interoperability between knowledge and associates to more relevant semantic information. In the case of differences between ontologies, how to reduce false matching and unsuccessful matching is a critical problem to be solved. Moreover, as the number of ontologies increases, the semantic relationship between ontologies becomes increasingly complex. Nevertheless, the current methods that solely find the similarity of names between concepts are no longer sufficient. Consequently, this paper proposes an ontology matching method based on semantic association. Accurate matching pairs are discovered by existing semantic knowledge, and then the potential semantic associations between concepts are mined according to the characteristics of the contextual structure. The matching method can better carry out matching work based on reliable knowledge. In addition, this paper introduces a probabilistic logic repair method, which can detect and repair the conflict of matching results, to enhance the availability and reliability of matching results. The experimental results show that the proposed method effectively improves the quality of matching between ontologies and saves time on repairing incorrect matching pairs. Besides, compared with the existing ontology matching systems, the proposed method has better stability.
Publication Date: 2024/11/01

Qinghua WU,Weitong LI, Vol.E107-D, No.11, pp.1422-1432
Multi-focus image fusion involves combining partially focused images of the same scene to create an all-in-focus image. Aiming at the problems of existing multi-focus image fusion algorithms that the benchmark image is difficult to obtain and the convolutional neural network focuses too much on the local region, a fusion algorithm that combines local and global feature encoding is proposed. Initially, we devise two self-supervised image reconstruction tasks and train an encoder-decoder network through multi-task learning. Subsequently, within the encoder, we merge the dense connection module with the PS-ViT module, enabling the network to utilize local and global information during feature extraction. Finally, to enhance the overall efficiency of the model, distinct loss functions are applied to each task. To preserve the more robust features from the original images, spatial frequency is employed during the fusion stage to obtain the feature map of the fused image. Experimental results demonstrate that, in comparison to twelve other prominent algorithms, our method exhibits good fusion performance in objective evaluation. Ten of the selected twelve evaluation metrics show an improvement of more than 0.28%. Additionally, it presents superior visual effects subjectively.
Publication Date: 2024/11/01

Yuan LI,Tingting HU,Ryuji FUCHIKAMI,Takeshi IKENAGA, Vol.E107-D, No.11, pp.1396-1407
1 millisecond (1-ms) vision systems are gaining increasing attention in diverse fields like factory automation and robotics, as the ultra-low delay ensures seamless and timely responses. Superpixel segmentation is a pivotal preprocessing to reduce the number of image primitives for subsequent processing. Recently, there has been a growing emphasis on leveraging deep network-based algorithms to pursue superior performance and better integration into other deep network tasks. Superpixel Sampling Network (SSN) employs a deep network for feature generation and employs differentiable SLIC for superpixel generation. SSN achieves high performance with a small number of parameters. However, implementing SSN on FPGAs for ultra-low delay faces challenges due to the final layer’s aggregation of intermediate results. To address this limitation, this paper proposes an aggregated to pipelined structure for FPGA implementation. The final layer is decomposed into individual final layers for each intermediate result. This architectural adjustment eliminates the need for memory to store intermediate results. Concurrently, the proposed structure leverages decomposed layers to facilitate a pipelined structure with pixel streaming input to achieve ultra-low latency. To cooperate with the pipelined structure, layer-partitioned memory architecture is proposed. Each final layer has dedicated memory for storing superpixel center information, allowing values to be read and calculated from memory without conflicts. Calculation results of each final layer are accumulated, and the result of each pixel is obtained as the stream reaches the last layer. Evaluation results demonstrate that boundary recall and under-segmentation error remain comparable to SSN, with an average label consistency improvement of 0.035 over SSN. From a hardware performance perspective, the proposed system processes 1000 FPS images with a delay of 0.947 ms/frame.
Publication Date: 2024/11/01

One of the main tasks in tax administration is debt management. The main goal of this function is tax due collection. Statements are processed in order to select strategies to use in the debt management process to optimise the debt collection process. This work proposes to carry out a data mining process to predict debts of taxpayers with high probability of non-payment. The data mining process identifies high-risk debts using a survival analysis on a dataset from a tax administration. Three groups of tax debtors with similar payment behaviour were identified and a success rate of up to 90% was reached in estimating the payment time of taxpayers. The concordance index (C-index) was used to determine the performance of the constructed model. The highest prediction rate reached was 90.37% corresponding to the third group.